Search Results (24,967)

In this work, Surface-Enhanced Raman Spectroscopy (SERS) was employed as an effective detection technique for folpet, characterized by its notable specificity and sensitivity. The investigation involved the use of UV–Vis, Raman, and SERS spectroscopy of folpet at different concentrations for a comprehensive study of plasmon-driven effects such as plasmon resonance, plasmon hybridization, and electric field enhancement resulting in the SERS’ intensification. Specifically, SERS detection of folpet solutions at concentrations below 100 µM is presented in detail by using Ag nanoparticles prepared with hydroxylamine reduction. The experimentation encompassed diverse conditions to optimize the detection process, with Raman spectra acquired for both folpet powder and aqueous solution of folpet at the natural pH. SERS analyses were conducted across a concentration range of 9.5 × 10⁻⁸ to 1.61 × 10⁻⁴ M, employing 532 nm excitation. The differences in the spectral profiles observed for folpet Raman powder and SERS are ascribed to N–S cleavage; these changes are attributed to plasmon catalysis induced by the used Ag nanoparticles. Transmission electron microscopy (TEM) was also important in the present analysis to better understand which mechanism of nanoparticles aggregation is more favorable for the SERS detection regarding the formation of hot spots in the suspension. Complementing the experimental data, the molecular structure and theoretical Raman spectra of the folpet molecule were calculated through density functional theory (DFT) methods. The outcomes of these calculations were crucial in the elucidation of folpet’s vibrational modes. The culmination of this research resulted in the successful detection of folpet, achieving a notable limit of detection at 4.78 × 10⁻⁸ M. This comprehensive approach amalgamates experimental and theoretical methodologies, offering significant insights into the detection capabilities and molecular characteristics of folpet via SERS analysis. Full article

Ultra-High-Performance Concrete (UHPC) and Ultra-High-Performance Fiber-Reinforced Concrete (UHPFRC) represent promising materials in the field of construction, offering exceptional strength and durability, making them ideal for the development of a wide range of infrastructure projects. One of the goals is to better understand the impact of each component of the materials on their key properties in the hardened state. This work examines the effect of the aggregate on the properties of UHPC and UHPFRC. This article provides test results for five compositions without fiber, and five compositions with 2% corrugated steel fiber. Three aggregate concentrations (0, 0.2, and 0.4 m3) and quartz sand with different maximum particle sizes (0.4 and 0.8 mm) were selected. It was found that the mechanical properties of the material, such as the steel fiber bond strength, compressive and axial tensile strength, fracture energy, and critical stress intensity factor, depend on both the concentration of the aggregate and the size of its particles. A novel mix-design parameter was proposed, which reflects the total surface area of the aggregate in the composition (Sagg,tot). The relationships between the parameter Sagg,tot and the mechanical characteristics of UHPC and UHPFRC were established. The steel fiber bond strength, axial tensile strength, and fracture energy-related parameters grew non-linearly when the parameter Sagg,tot increased. When the parameter Sagg,tot was changed from 0 to 12.38∙103 m2, the fiber bond strength increased by 1.38 times. The axial tensile strength and total fracture energy of the UHPFRC increased by 1.48 and 1.63 times, respectively. The compressive strength changed linearly and increased by 1.12 times. The improvement in the mechanical properties of the material was associated with an increase in the friction force between the fiber and the matrix, which was confirmed by the formation of a greater number of scratches on the surface of the fiber with an increasing value of the parameter Sagg,tot. The deformation characteristics, such as modulus of elasticity, Poisson’s ratio, and drying shrinkage strain, were determined solely by the volumetric concentration of the aggregate, as in conventional concrete. An increase in the aggregate volume content from 0 to 0.4 m3 led to an increase in the modulus of elasticity of 1.41–1.44 times, and a decrease in the ultimate shrinkage strain of almost 2 times. The dependencies obtained in this work can be used to predict the properties of UHPC and UHPFRC, taking into account the type and volume concentration of the aggregate. Full article

Drought monitoring is a critical environmental challenge, particularly in regions where irrigated agricultural intensification and urban expansion pressure water resources. This study assesses the impact of these activities on drought dynamics in Morocco’s Oum Er-Rbia (OER) watershed from 2002 to 2022, using the newly developed Watershed Integrated Multi-Drought Index (WIMDI), through Google Earth Engine (GEE). WIMDI integrates several drought indices, including SMCI, ESI, VCI, TVDI, SWI, PCI, and SVI, via a localized weighted averaging model (LOWA). Statistical validation against various drought-type indices including SPI, SDI, SEDI, and SMCI showed WIMDI’s strong correlations (r-values up to 0.805) and lower RMSE, indicating superior accuracy. Spatiotemporal validation against aggregated drought indices such as VHI, VDSI, and SDCI, along with time-series analysis, confirmed WIMDI’s robustness in capturing drought variability across the OER watershed. These results highlight WIMDI’s potential as a reliable tool for effective drought monitoring and management across diverse ecosystems and climates. Full article

To investigate the impact of diverse soil characteristics and surface irregularities on interfacial shear strength attributes, a large-scale straight shear apparatus and particle flow software were employed to conduct interfacial shear experiments with varying soil properties and surface irregularities. The results demonstrated that, under an identical R and normal stress conditions, the clay and silty clay shear stress–displacement curves exhibited strain softening, while the silt curve exhibited strain hardening. An increase in R can markedly enhance the peak shear strength at the interface, although a critical value exists beyond which this effect is no longer observed. The R_c is primarily contingent upon the soil properties. Numerical simulations demonstrate that the internal shear displacement and deformation resulting from the diverse soil properties are distinct. Clay particles are constituted of varying-sized particle aggregates that collectively resist shear. Silt particles resist shear through interfacial friction generated by shear. The practicality of Duncan and Clough’s constitutive model for interfacial shear with roughness influence is verified, and the constitutive model under strain hardening is modified. Full article

This paper combines self-organizing mapping (SOM) and a long short-term memory network (SOM-LSTM) to construct an audio-based motor-fault diagnosis system for identifying the operating states of a rotary motor. This paper first uses an audio signal collector to measure the motor sound signal data, uses fast Fourier transform (FFT) to convert the actual measured sound–time-domain signal into a frequency-domain signal, and normalizes and calibrates the frequency-domain signal to ensure the consistency and accuracy of the signal. Secondly, the SOM is used to further analyze the characterized frequency-domain waveforms in order to reveal the intrinsic structure and pattern of the data. The LSTM network is used to process the secondary data generated via SOM. Dimensional data aggregation and the prediction of sequence data in long-term dependencies accurately identify different operating states and possible abnormal patterns. This paper also uses the experimental design of the Taguchi method to optimize the parameters of SOM-LSTM in order to increase the execution efficiency of fault diagnosis. Finally, the fault diagnosis system is applied to the real-time monitoring of the motor operation, the work of identifying the motor-fault type is performed, and tests under different loads and environments are attempted to evaluate its feasibility. The completion of this paper provides a diagnostic strategy that can be followed when it comes to motor faults. Through this fault diagnosis system, abnormal conditions in motor equipment can be detected, which can help with preventive maintenance, make work more efficient and save a lot of time and costs, and improve the industry’s ability to monitor motor operation information. Full article

Neurodegenerative diseases (NDs), like amyotrophic lateral sclerosis (ALS), Alzheimer’s disease (AD), and Parkinson’s disease (PD), primarily affect the central nervous system, leading to progressive neuronal loss and motor and cognitive dysfunction. However, recent studies have revealed that muscle tissue also plays a significant role in these diseases. ALS is characterized by severe muscle wasting as a result of motor neuron degeneration, as well as alterations in gene expression, protein aggregation, and oxidative stress. Muscle atrophy and mitochondrial dysfunction are also observed in AD, which may exacerbate cognitive decline due to systemic metabolic dysregulation. PD patients exhibit muscle fiber atrophy, altered muscle composition, and α-synuclein aggregation within muscle cells, contributing to motor symptoms and disease progression. Systemic inflammation and impaired protein degradation pathways are common among these disorders, highlighting muscle tissue as a key player in disease progression. Understanding these muscle-related changes offers potential therapeutic avenues, such as targeting mitochondrial function, reducing inflammation, and promoting muscle regeneration with exercise and pharmacological interventions. This review emphasizes the importance of considering an integrative approach to neurodegenerative disease research, considering both central and peripheral pathological mechanisms, in order to develop more effective treatments and improve patient outcomes. Full article

A tetraphenylethylene (TPE) derivative, TPEPhDAT, modified by diaminotriazine (DAT), was prepared by successive Suzuki–Miyaura coupling and ring-closing reactions. This compound exhibits aggregation-induced emission enhancement (AIEE) properties in the DMSO/MeOH system, with a fluorescence emission intensity in the aggregated state that is 5-fold higher than that of its counterpart in a dilute solution. Moreover, the DAT structure of the molecule is a good acceptor of protons; thus, the TPEPhDAT molecule exhibits acid-responsive fluorescence. TPEPhDAT was protonated by trifluoroacetic acid (TFA), leading to fluorescence quenching, which was reversibly restored by treatment with ammonia (on–off switch). Time-dependent density functional theory (TDDFT) computational studies have shown that protonation enhances the electron-withdrawing capacity of the triazine nucleus and reduces the bandgap. The protonated TPEPhDAT conformation became more distorted, and the fluorescence lifetime was attenuated, which may have produced a twisted intramolecular charge transfer (TICT) effect, leading to fluorescence redshift and quenching. MeOH can easily remove the protonated TPEPhDAT, and this acid-induced discoloration and erasable property can be applied in anti-counterfeiting. Full article

Accurate load forecasting, especially in the short term, is crucial for the safe and stable operation of power systems and their market participants. However, as modern power systems become increasingly complex, the challenges of short-term load forecasting are also intensifying. To address this challenge, data-driven deep learning techniques and load aggregation technologies have gradually been introduced into the field of load forecasting. However, data quality issues persist due to various factors such as sensor failures, unstable communication, and susceptibility to network attacks, leading to data gaps. Furthermore, in the domain of aggregated load forecasting, considering the potential interactions among aggregated loads can help market participants engage in cross-market transactions. However, aggregated loads often lack clear geographical locations, making it difficult to predefine graph structures. To address the issue of data quality, this study proposes a model named adversarial graph convolutional imputation network (AGCIN), combined with local and global correlations for imputation. To tackle the problem of the difficulty in predefining graph structures for aggregated loads, this study proposes a learnable adjacency matrix, which generates an adaptive adjacency matrix based on the relationships between different sequences without the need for geographical information. The experimental results demonstrate that the proposed imputation method outperforms other imputation methods in scenarios with random and continuous missing data. Additionally, the prediction accuracy of the proposed method exceeds that of several baseline methods, affirming the effectiveness of our approach in imputation and prediction, ultimately enhancing the accuracy of aggregated load forecasting. Full article

The aim of this research has been to enable the management of trustworthy relationships between stakeholders, service providers, and physical assets, which are required in critical energy and mobility cyber–physical systems (CPS) applications. The achieved novel contribution is the concept of trustworthy communities with respective experimental solutions, which are developed by relying on verifiable credentials, smart contracts, trust over IP, and an Ethereum-based distributed ledger. The provided trustworthy community solutions are validated by executing them in two practical use cases, which are called energy flexibility and hunting safety. The energy flexibility case validation considered the execution of the solutions with one simulated and two real buildings with the energy flexibility aggregation platform, which was able to trade the flexibilities in an energy flexibility marketplace. The provided solutions were executed with a hunting safety smartphone application for a hunter and the smartwatch of a person moving around in the forest. The evaluations indicate that conceptual solutions for trustworthy communities fulfill the purpose and contribute toward making energy flexibility trading and hunting safety possible and trustworthy enough for participants. A trustworthy community solution is required to make value sharing and usage of critical energy resources and their flexibilities feasible and secure enough for their owners as part of the energy flexibility community. Sharing the presence and location in mobile conditions requires a trustworthy community solution because of security and privacy reasons, but it can also save lives in real-life elk hunting cases. During the evaluations, the need for further studies related to performance, scalability, community applications, verifiable credentials with wallets, sharing of values and incentives, authorized trust networks, dynamic trust situations, time-sensitive behavior, autonomous operations with smart contracts through security assessment, and applicability have been detected. Full article

Clarifying the temporal and spatial characteristics of regional carbon emissions and low-carbon efficiency is of great significance for the realization of carbon peaking and carbon neutrality. This study calculated the carbon emissions in Sichuan Province from 2015 to 2022 based on four major units: energy activity, industrial production, forestry activity, and waste disposal, and its time evolution characteristics and key sources were investigated. Meanwhile, based on the Super-SBM-Undesirable model, the low-carbon efficiency of Sichuan Province and its 21 cities (states) was evaluated, and its spatial heterogeneity characteristics were investigated. The empirical results reveal the following: (1) energy activity was the main contributor to regional carbon emissions, with thermal power generation and industrial energy terminal consumption as the key sectors. Inter-regional power allocation could indirectly reduce the regional emission intensity. The carbon emissions of industrial production showed significant aggregation in cement and steel production. The forest carbon sink had a significant effect on alleviating the regional greenhouse effect. The carbon emissions of waste disposal were small. (2) From 2015 to 2022, the low-carbon efficiency of Sichuan Province showed an overall upward trend. Chengdu had a high level of economic development, a reasonable industrial organization, and a continuous increase in its urban greening rate. Heavy industrial cities such as Panzhihua and Deyang made great efforts to eliminate backward production capacity and low-carbon transformation of key industries. Therefore, they were the first mover advantage regions of low-carbon transformation. Zigong, Mianyang, Suining, and Leshan enjoyed favorable preferential policies and energy-saving space, and were developmental regions of low-carbon transformation. But they need to actively deal with the problem of industrial solidification. The low-carbon efficiency of plateau areas in western Sichuan was relatively low, but they have unique resource endowment advantages in clean energy such as hydropower, so the development potential is strong. Cities such as Ya’an and Bazhong faced a series of challenges such as weak geographical advantages and the risk of pollution haven. They were potential regions of low-carbon transformation. Full article

Through the use of recycled aggregates, the construction industry can mitigate its environmental impact. A key consideration for concrete structural engineers when designing and constructing concrete structures is compressive strength. This study aims to accurately forecast the compressive strength of recycled aggregate concrete (RAC) using machine learning techniques. We propose a simplified approach that incorporates a two-layer stacked ensemble learning model to predict RAC compressive strength. In this framework, the first layer consists of ensemble models acting as base learners, while the second layer utilizes a random forest (RF) model as the meta-learner. A comparative analysis with four other ensemble learning models demonstrates the superior performance of the proposed stacked model in effectively integrating predictions from the base learners, resulting in enhanced model accuracy. The model achieves a low mean absolute error (MAE) of 2.599 MPa, a root mean squared error (RMSE) of 3.645 MPa, and a high R-squared (R²) value of 0.964. Additionally, a Shapley (SHAP) additive explanation analysis reveals the influence and interrelationships of various input factors on the compressive strength of RAC, aiding design and construction professionals in optimizing raw material content during the RAC design and production process. Full article

The interplay between land management and real estate market dynamics is critical for sustainable development. This study employs behavioral finance theory to explore how irrational behaviors among key market participants, including developers, consumers, and brokers, influence housing prices in China. By examining decision aggregation processes and sociocultural influences, we identify significant behavioral factors such as overconfidence, herding behavior, and availability bias that contribute to real estate price fluctuations. Our empirical analysis, based on data from 2001 to 2018, reveals how these behaviors impact market outcomes and provides insights for improving land administration systems. The findings offer valuable perspectives for policy and strategy development aimed at stabilizing housing markets, promoting sustainable real estate practices, and supporting the achievement of sustainable development goals (SDGs). This research underscores the importance of integrating behavioral finance into land management to enhance the efficiency and security of land tenure systems. Full article

A range of remote sensing platforms provide high spatial and temporal resolution insights which are useful for monitoring vegetation growth. Very few studies have focused on fruit orchards, largely due to the inherent complexity of their structure. Fruit trees are mixed with inter-rows that can be grassed or non-grassed, and there are no standard protocols for ground measurements suitable for the range of crops. The assessment of biophysical variables (BVs) for fruit orchards from optical satellites remains a significant challenge. The objectives of this study are as follows: (1) to address the challenges of extracting and better interpreting biophysical variables from optical data by proposing new ground measurements protocols tailored to various orchards with differing inter-row management practices, (2) to quantify the impact of the inter-row at the Sentinel pixel scale, and (3) to evaluate the potential of Sentinel 2 data on BVs for orchard development monitoring and the detection of key phenological stages, such as the flowering and fruit set stages. Several orchards in two pedo-climatic zones in southeast France were monitored for three years: four apricot and nectarine orchards under different management systems and nine cherry orchards with differing tree densities and inter-row surfaces. We provide the first comparison of three established ground-based methods of assessing BVs in orchards: (1) hemispherical photographs, (2) a ceptometer, and (3) the Viticanopy smartphone app. The major phenological stages, from budburst to fruit growth, were also determined by in situ annotations on the same fields monitored using Viticanopy. In parallel, Sentinel 2 images from the two study sites were processed using a Biophysical Variable Neural Network (BVNET) model to extract the main BVs, including the leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and fraction of green vegetation cover (FCOVER). The temporal dynamics of the normalised FAPAR were analysed, enabling the detection of the fruit set stage. A new aggregative model was applied to data from hemispherical photographs taken under trees and within inter-rows, enabling us to quantify the impact of the inter-row at the Sentinel 2 pixel scale. The resulting value compared to BVs computed from Sentinel 2 gave statistically significant correlations (0.57 for FCOVER and 0.45 for FAPAR, with respective RMSE values of 0.12 and 0.11). Viticanopy appears promising for assessing the PAI (plant area index) and FCOVER for orchards with grassed inter-rows, showing significant correlations with the Sentinel 2 LAI (R² of 0.72, RMSE 0.41) and FCOVER (R² 0.66 and RMSE 0.08). Overall, our results suggest that Sentinel 2 imagery can support orchard monitoring via indicators of development and inter-row management, offering data that are useful to quantify production and enhance resource management. Full article

Objectives: The multifaceted impact of Traumatic brain injury (TBI) encompasses complex healthcare costs and diverse health complications, including the emergence of Post-Traumatic Seizures (PTS). In this study, our goal was to discern and elucidate the incidence and risk factors implicated in the pathogenesis of PTS. We hypothesize that the development of PTS following TBI varies based on the type and severity of TBI. Methods: Our study leveraged the Nationwide Inpatient Sample (NIS) to review primary TBI cases spanning 2016–2020 in the United States. Admissions featuring the concurrent development of seizures during the admission were queried. The demographic variables, concomitant diagnoses, TBI subtypes, hospital charges, hospital length of stay (LOS), and mortality were analyzed. Results: The aggregate profile of TBI patients delineated a mean age of 61.75 (±23.8) years, a male preponderance (60%), and a predominantly White demographic (71%). Intriguingly, patients who encountered PTS showcased extended LOS (7.5 ± 9.99 vs. 6.87 ± 10.98 days, p < 0.001). Paradoxically, PTS exhibited a reduced overall in-hospital mortality (6% vs. 8.1%, p < 0.001). Notably, among various TBI subtypes, traumatic subdural hematoma (SDH) emerged as a predictive factor for heightened seizure development (OR 1.38 [1.32–1.43], p < 0.001). Conclusions: This rigorous investigation employing an extensive national database unveils a 4.95% incidence of PTS, with SDH accentuating odds of seizure risk by OR: 1.38 ([1.32–1.43], p < 0.001). The paradoxical correlation between lower mortality and PTS is expected to be multifactorial and necessitates further exploration. Early seizure prophylaxis, prompt monitoring, and equitable healthcare provision remain pivotal avenues for curbing seizure incidence and comprehending intricate mortality trends. Full article

In the rapidly advancing domain of Intelligent Connected Vehicles (ICVs), multimodal Federated Learning (FL) presents a powerful methodology to harness diverse data sources, such as sensors, cameras, and Vehicle-to-Everything (V2X) communications, without compromising data privacy. Despite its potential, the presence of Byzantine adversaries–malicious participants who contribute incorrect or misleading updates–poses a significant challenge to the robustness and reliability of the FL process. This paper proposes a Byzantine-robust multimodal FL framework specifically designed for ICVs. Our framework integrates a robust aggregation mechanism to mitigate the influence of adversarial updates, a multimodal fusion strategy to effectively manage and combine heterogeneous input data, and a global optimization objective that accommodates the presence of Byzantine clients. The theoretical foundation of the framework is established through formal definitions and equations, demonstrating its ability to maintain reliable and accurate learning outcomes despite adversarial disruptions. Extensive experiments highlight the framework’s efficacy in preserving model performance and resilience in real-world ICV environments. Full article